where DNN is typically trained to maximize the log probability of the phoneme alignment or minimize the cross-entropy error, and \(s\)  is the target speaker with an x-vector [1]} embedding \(z_s\) . In this study, the original ResNet18 model is considered as our baseline. Next, affine transformation layers are inserted into ResNet18 network to build the speaker conditioned acoustic model. The scale and bias factors of AT are estimated by a two-layer fully connected network \(h\)  based on x-vector \(z_s\)  as:
\((\alpha _{i,c},\; \beta _{i,c}) = h(z_s)\) 
